Assignment #1

library(tidyverse)         # for graphing and data cleaning
library(tidymodels)        # for modeling
library(naniar)            # for analyzing missing values
library(vip)               # for variable importance plots
theme_set(theme_minimal()) # Lisa's favorite theme

hotels <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv')

When you finish the assignment, remove the # from the options chunk at the top, so that messages and warnings aren’t printed. If you are getting errors in your code, add error = TRUE so that the file knits. I would recommend not removing the # until you are completely finished.

Setting up Git and GitHub in RStudio

Read the Quick Intro section of the Using git and GitHub in R Studio set of Course Materials. Set up Git and GitHub and create a GitHub repo and associated R Project (done for you when you clone the repo) for this homework assignment. Put this file into the project. You should always open the R Project (.Rproj) file when you work with any of the files in the project.

Task: Below, post a link to your GitHub repository. Github Repo

Creating a website

You’ll be using RStudio to create a personal website to showcase your work from this class! Start by watching the Sharing on Short Notice webinar by Alison Hill and Desirée De Leon of RStudio. This should help you choose the type of website you’d like to create.

Once you’ve chosen that, you might want to look through some of the other Building a website resources I posted on the resources page of our course website. I highly recommend making a nice landing page where you give a brief introduction of yourself.

Tasks:

• Include a link to your website below. (If anyone does not want to post a website publicly, please talk to me and we will find a different solution).
  My Website

• Listen to at least the first 20 minutes of “Building a Career in Data Science, Chapter 4: Building a Portfolio”. Go to the main podcast website and navigate to a podcast provider that works for you to find that specific episode. Write 2-3 sentences reflecting on what they discussed and why creating a website might be helpful for you.

Having something that is public can show that you can do the things that you say that you can do. I think that this will be useful to me in forcing me to make my code and my projects more accessible to everyone.

Machine Learning review and intro to tidymodels

Read through and follow along with the Machine Learning review with an intro to the tidymodels package posted on the Course Materials page.

Tasks:

1. Read about the hotel booking data, hotels, on the Tidy Tuesday page it came from. There is also a link to an article from the original authors. The outcome we will be predicting is called is_canceled.

• Without doing any analysis, what are some variables you think might be predictive and why?
  _ What are some problems that might exist with the data? You might think about how it was collected and who did the collecting.
  
• If we construct a model, what type of conclusions will be able to draw from it?

I think that the previous cancellations and previous bookings not canceled variables will be the most predictive because it can tell us if the person booking the room has a pattern of canceling bookings. Also the difference in the date booked and when the booking is for might be quite predictive because a booking that is further away from the present gives more time to have plans change for people. One problem with the dataset could be the reservation status variable because it seems redundant. There is also no id on each of the inputs, so I don’t know if anything has been double counted. If we construct a model we will be able to draw out who is more likely to cancel their reservations at the hotels.

2. Create some exploratory plots or table summaries of the data, concentrating most on relationships with the response variable. Keep in mind the response variable is numeric, 0 or 1. You may want to make it categorical (you also may not). Be sure to also examine missing values or other interesting values.

hotels %>% 
  group_by(previous_cancellations) %>% 
  summarise(count = n())

hotels %>% 
  add_n_miss() %>% 
  filter(n_miss_all == 1) %>% 
  select(n_miss_all, children)

hotels %>% 
  ggplot(aes(x = hotel, fill = customer_type))+
  geom_bar()

hotels %>% 
  ggplot(aes(x = arrival_date_month, fill = arrival_date_month))+
  geom_bar()

3. First, we will do a couple things to get the data ready, including making the outcome a factor (needs to be that way for logistic regression), removing the year variable and some reservation status variables, and removing missing values (not NULLs but true missing values). Split the data into a training and test set, stratifying on the outcome variable, is_canceled. Since we have a lot of data, we’re going to split the data 50/50 between training and test. I have already set.seed() for you. Be sure to use hotels_mod in the splitting.

hotels_mod <- hotels %>% 
  mutate(is_canceled = as.factor(is_canceled)) %>% 
  mutate(across(where(is.character), as.factor)) %>% 
  select(-arrival_date_year,
         -reservation_status,
         -reservation_status_date) %>% 
  add_n_miss() %>% 
  filter(n_miss_all == 0) %>% 
  select(-n_miss_all)

set.seed(494)
hotel_split <- initial_split(hotels_mod, prop = .5, strata = is_canceled)
hotel_training <- training(hotel_split)
hotel_testing <- testing(hotel_split)

4. In this next step, we are going to do the pre-processing. Usually, I won’t tell you exactly what to do here, but for your first exercise, I’ll tell you the steps.

• Set up the recipe with is_canceled as the outcome and all other variables as predictors (HINT: ~.).
  
• Use a step_XXX() function or functions (I think there are other ways to do this, but I found step_mutate_at() easiest) to create some indicator variables for the following variables:children,babies, andprevious_cancellations`. So, the new variable should be a 1 if the original is more than 0 and 0 otherwise. Make sure you do this in a way that accounts for values that may be larger than any we see in the dataset.
  
• For the agent and company variables, make new indicator variables that are 1 if they have a value of NULL and 0 otherwise.
• Use fct_lump_n() to lump together countries that aren’t in the top 5 most occurring.
• If you used new names for some of the new variables you created, then remove any variables that are no longer needed.
• Use step_normalize() to center and scale all the non-categorical predictor variables. (Do this BEFORE creating dummy variables. When I tried to do it after, I ran into an error - I’m still investigating why.)
• Create dummy variables for all factors/categorical predictor variables (make sure you have -all_outcomes() in this part!!).
  
• Use the prep() and juice() functions to apply the steps to the training data just to check that everything went as planned.

hotel_recipe <- recipe(is_canceled ~ . , data = hotel_training) %>% 
  step_mutate(children = as.factor(ifelse(children == 0,0,1)),
  babies = as.factor(ifelse(babies == 0,0,1)),
  previous_cancellations = as.factor(ifelse(previous_cancellations == 0,0,1)),
  agent = as.factor(ifelse(agent == "NULL",0,1)),
  company = as.factor(ifelse(company == "NULL",0,1)),
  country = fct_lump_n(country, 5)) %>% 
  step_normalize(all_predictors(),
                 -all_nominal()) %>% 
  step_dummy(all_nominal(),
             -all_outcomes())

hotel_recipe %>% 
  prep(hotel_training) %>% 
  juice()

5. In this step we will set up a LASSO model and workflow.

• In general, why would we want to use LASSO instead of regular logistic regression? (HINT: think about what happens to the coefficients).

We want to use LASSO instead of regular logistic regression in order to compress coefficients that would lead to overfitting of the model.

* Define the model type, set the engine, set the penalty argument to tune() as a placeholder, and set the mode.
* Create a workflow with the recipe and model.

hotel_lasso <- logistic_reg(mixture = 1) %>% 
  set_engine("glmnet") %>% 
  set_args(penalty = tune()) %>% 
  set_mode("classification")

hotel_lasso

## Logistic Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = tune()
##   mixture = 1
## 
## Computational engine: glmnet

hotel_lasso_wf <- 
  workflow() %>% 
  add_recipe(hotel_recipe) %>% 
  add_model(hotel_lasso)

hotel_lasso_wf

## == Workflow ====================================================================
## Preprocessor: Recipe
## Model: logistic_reg()
## 
## -- Preprocessor ----------------------------------------------------------------
## 3 Recipe Steps
## 
## * step_mutate()
## * step_normalize()
## * step_dummy()
## 
## -- Model -----------------------------------------------------------------------
## Logistic Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = tune()
##   mixture = 1
## 
## Computational engine: glmnet

6. In this step, we’ll tune the model and fit the model using the best tuning parameter to the entire training dataset.

• Create a 5-fold cross-validation sample. We’ll use this later. I have set the seed for you.
  
• Use the grid_regular() function to create a grid of 10 potential penalty parameters (we’re keeping this sort of small because the dataset is pretty large). Use that with the 5-fold cv data to tune the model.
  
• Use the tune_grid() function to fit the models with different tuning parameters to the different cross-validation sets.
  
• Use the collect_metrics() function to collect all the metrics from the previous step and create a plot with the accuracy on the y-axis and the penalty term on the x-axis. Put the x-axis on the log scale.
  
• Use the select_best() function to find the best tuning parameter, fit the model using that tuning parameter to the entire training set (HINT: finalize_workflow() and fit()), and display the model results using pull_workflow_fit() and tidy(). Are there some variables with coefficients of 0?

Yes there are some 0 coefficients: arrival_date_month_September, market_segment_Groups, market_segment_Unidentified, distribution_channel_Undefined, assigned_room_type_L.

set.seed(494) # for reproducibility
hotel_cv <- vfold_cv(hotel_training, v = 5)

penalty_grid <- grid_regular(penalty(),
                             levels = 10)

penalty_grid

hotel_lasso_tune <- 
  hotel_lasso_wf %>% 
  tune_grid(
    resamples = hotel_cv,
    grid = penalty_grid
    )

hotel_lasso_tune

hotel_lasso_tune %>% 
  collect_metrics() %>% 
  filter(.metric == "accuracy") %>% 
  ggplot(aes(x = penalty, y = mean)) +
  geom_point() +
  geom_line() +
  scale_x_log10(
   breaks = scales::trans_breaks("log10", function(x) 10^x),
   labels = scales::trans_format("log10",scales::math_format(10^.x))) +
  labs(x = "penalty", y = "accuracy")

hotel_lasso_tune %>% 
  show_best(metric = "accuracy")

best <- hotel_lasso_tune %>% 
  select_best(metric = "accuracy")

hotel_lasso_final_wf <- hotel_lasso_wf %>% 
  finalize_workflow(best)

hotel_lasso_final_wf

## == Workflow ====================================================================
## Preprocessor: Recipe
## Model: logistic_reg()
## 
## -- Preprocessor ----------------------------------------------------------------
## 3 Recipe Steps
## 
## * step_mutate()
## * step_normalize()
## * step_dummy()
## 
## -- Model -----------------------------------------------------------------------
## Logistic Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = 1e-10
##   mixture = 1
## 
## Computational engine: glmnet

hotel_lasso_final_mod <- hotel_lasso_final_wf %>% 
  fit(data = hotel_training)

hotel_lasso_final_mod %>% 
  pull_workflow_fit() %>% 
  tidy() 

7. Now that we have a model, let’s evaluate it a bit more. All we have looked at so far is the cross-validated accuracy from the previous step.

• Create a variable importance graph. Which variables show up as the most important? Are you surprised?

hotel_lasso_final_mod %>% 
  pull_workflow_fit() %>% 
  vip()

The most important variables are if the room reserved is type, if the deposit was non refundable, and previous cancellations. I am slightly surprised by the importance of the room type, but the other two make lots of sense.

• Use the last_fit() function to fit the final model and then apply it to the testing data. Report the metrics from the testing data using the collet_metrics() function. How do they compare to the cross-validated metrics?

hotel_lasso_test <- hotel_lasso_final_wf %>% 
  last_fit(hotel_split)

hotel_lasso_test %>% 
  collect_metrics()

Both of these are higher than the cv accuracy, but only by a tiny bit. 0.813 vs 0.815.

• Use the collect_predictions() function to find the predicted probabilities and classes for the test data. Save this to a new dataset called preds. Then, use the conf_mat() function from dials (part of tidymodels) to create a confusion matrix showing the predicted classes vs. the true classes. What is the true positive rate (sensitivity)? What is the true negative rate (specificity)? See this Wikipedia reference if you (like me) tend to forget these definitions.

The sensitivity is 65%. The specificity is 91.2%.

preds <- hotel_lasso_test %>% 
  collect_predictions()

preds %>% 
  conf_mat(is_canceled, .pred_class)

##           Truth
## Prediction     0     1
##          0 34291  7747
##          1  3292 14363

• Use the preds dataset you just created to create a density plot of the predicted probabilities of canceling (the variable is called .pred_1), filling by is_canceled. Use an alpha = .5 and color = NA in the geom_density(). Answer these questions: a. What would this graph look like for a model with an accuracy that was close to 1? b. Our predictions are classified as canceled if their predicted probability of canceling is greater than .5. If we wanted to have a high true positive rate, should we make the cutoff for predicted as canceled higher or lower than .5? c. What happens to the true negative rate if we try to get a higher true positive rate?

a. If the accuracy was close to 1 there would be two very high peaks at 0 and 1 with very little density in the middle. b. We would need to make it higher than .5 to have a higher sensitivity. c. If we try to have a higher sensitivity we would lower our specificity.

preds %>% 
  ggplot(aes(x = .pred_1, fill = is_canceled))+
  geom_density(alpha = 0.5, color = NA)

8. Let’s say that this model is going to be applied to bookings 14 days in advance of their arrival at each hotel, and someone who works for the hotel will make a phone call to the person who made the booking. During this phone call, they will try to assure that the person will be keeping their reservation or that they will be canceling in which case they can do that now and still have time to fill the room. How should the hotel go about deciding who to call? How could they measure whether it was worth the effort to do the calling? Can you think of another way they might use the model?

I think that using this model would be beneficial in calling the people who are predicted to cancel if the type of hotel that you run can easily fill the room in 14 days. You could measure the efficacy of this by measuring how often when someone you call actually cancels you are able to fill that room for the time they booked it for. Another way that you could use this model is seeing how to lower the amounts of people who cancel. Or you can use it to assign rooms, so that the best rooms for you are always filled.

9. How might you go about questioning and evaluating the model in terms of fairness? Are there any questions you would like to ask of the people who collected the data?

I think that the most important thing is asking how the previous cancellations variable was acquired, making sure that it was not acquired in an unfair fashion. Also, I would want to make sure there is no bias that is discriminating against families with children or people that originate from a certain country.

Bias and Fairness

Listen to Dr. Rachel Thomas’s Bias and Fairness lecture. Write a brief paragraph reflecting on it. You might also be interested in reading the ProPublica article Dr. Thomas references about using a tool called COMPAS to predict recidivism. Some questions/ideas you might keep in mind:

• Did you hear anything that surprised you?
  
• Why is it important that we pay attention to bias and fairness when studying data science?
  
• Is there a type of bias Dr. Thomas discussed that was new to you? Can you think about places you have seen these types of biases?

It is important to pay attention to bias and fairness when studying data science so that our models are actually predicting what we want them to. If there are unfair biases that go into our models then our models will amplify these biases and only output the bias that is put in. Thus, the model is not actually outputting what we want it to. Measurement bias was a new type of bias that I had never heard of. I think that we don’t normally think about the bias of what we are measuring, but rather the bias in how we analyze the data. We often think that the data itself cannot be wrong, but if it is measured improperly/ if we are measuring things we don’t mean to measure the data can be flawed.